Multi-round Dialogue Translation via Semantic Intermediary

The patent introduces an intermediate representation layer that captures semantic meaning independently of language. The encoder transforms source language input into language-independent semantic representations, which then guide the decoder to generate target language output. This intermediary semantic layer prevents direct translation errors by decoupling semantic understanding from language-specific expressions.

The patent replaces traditional machine translation mechanisms with a neural sequence-to-sequence model that uses attention mechanisms and language-independent representations. Instead of relying on rule-based or statistical translation methods, the system uses deep learning to capture contextual semantics and generate linguistically appropriate outputs, thereby preserving semantic coherence across language boundaries.

The patent creates a universal dialogue model architecture that can be trained on high-resource language corpora and then applied to low-resource languages through translation. The language-independent semantic representations allow the model to learn general dialogue patterns from abundant high-resource data, which then benefit low-resource language applications without requiring large amounts of language-specific training data.

The patent changes the parameter space by introducing language-independent semantic embeddings and attention mechanisms that focus on contextual meaning rather than surface-level linguistic features. This parameter transformation allows the model to generalize across languages effectively, achieving good performance on low-resource languages by leveraging patterns learned from high-resource languages.

The patent maintains continuous contextual understanding across multiple dialogue turns by incorporating attention mechanisms that weigh the importance of different historical utterances. The model processes the entire conversation history continuously, updating semantic representations at each turn to reflect the evolving context, thereby preserving coherence while enabling efficient generation.

The patent implements feedback mechanisms where the generated output at each turn is fed back into the contextual representation for subsequent turns. This allows the model to maintain consistency with previous exchanges and adjust its responses based on the evolving dialogue state, ensuring semantic coherence across multiple turns while maintaining translation efficiency.